We propose a novel scene flow estimation approach to capture and infer 3D motions from point clouds. Estimating 3D motions for point clouds is challenging, since a point cloud is unordered and its density is significantly non-uniform. Such …
We introduce the idea of using learnable neighbor-to-grid soft assignment in grid-based aggregation functions for the task of 3D semantic segmentation. Previous methods in literature operate on a predefined geometric grid such as local volume …
Understanding videos is challenging in computer vision. In particular, the large memory footprint of an untrimmed video makes most tasks infeasible to train endto-end without dropping part of the input data. To cope with the memory limitation of …
Point cloud architecture design has become a crucial problem for 3D deep learning. Several efforts exist to manually design architectures with high accuracy in point cloud tasks such as classification, segmentation, and detection. Recent progress in …
In autonomous driving pipelines, perception modules provide a visual understanding of the surrounding road scene. Among the perception tasks, vehicle detection is of paramount importance for a safe driving as it identifies the position of other …
Tracking vehicles in LIDAR point clouds is a challenging task due to the sparsity of the data and the dense search space. The lack of structure in point clouds impedes the use of convolution and correlation filters usually employed in 2D object …
In this work, we propose a method for three-dimensional (3D) reconstruction of wide crime scene, based on a Simultaneous Localization and Mapping (SLAM) approach.